The Hundred-Page Machine Learning Book
by Andriy Burkov
Burkov's impossibly concise distillation of machine learning into 100 pages—math, intuition, and practical algorithms for practitioners.
"Let's start by telling the truth: machines don't learn".
Editorial Summary
Andriy Burkov's The Hundred-Page Machine Learning Book accomplishes the seemingly impossible task of condensing the entire field of machine learning into a readable, mathematically rigorous guide. The Quebec-based machine learning expert and team leader at TalentNeuron covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis, dimensionality reduction, autoencoders, transfer learning, feature engineering, and hyperparameter tuning. Burkov doesn't shy away from mathematical equations while maintaining clarity through intuitive explanations and practical examples, supported by a continuously updated wiki with additional resources. The book has been translated into eleven languages and is used as a textbook at universities worldwide, endorsed by industry leaders including Peter Norvig of Google and Aurélien Géron, author of the bestselling Hands-On Machine Learning.
Perspective
"Reading this book feels like getting a masterclass briefing from a practitioner who has distilled years of experience into every sentence—there's no wasted space, no filler, just dense, actionable knowledge that transforms complex algorithms into understandable tools. Burkov's distinctive contribution is his mathematical completeness within extreme brevity, refusing to sacrifice equations and formal notation despite the space constraints, creating a reference that's both rigorous and accessible. Software engineers and data scientists looking to quickly understand the mathematical foundations behind machine learning algorithms will find a comprehensive overview that covers everything from linear regression to deep learning in a format they can absorb in a week."
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